Comparison of box-jenkins time series and ANN in predicting total dissolved solid at the Zāyandé-Rūd River, Iran

Abstract

We applied the Box-Jenkins time series model and artificial neural network (ANN) in the framework of a multilayer perceptron (MLP) to predict the total dissolved solids (TDS) in the Zāyandé-Rūd River, Esfahan province, Iran. The MLP inputs were total hardness (TH), bicarbonate (HCO3), sulfate (SO42-), chloride (Cl-), Sodium (Na+), and Calcium (Ca2+), which were monitored over 9 years by the Esfahan Water Authority. The Autoregressive Integrate Moving Average (ARIMA) (2, 0, 3) (2, 0, 2) time series model with the lowest Akaike factor was selected. The coefficient of determination (R2) and index of agreement (IA) between the measured and predicted data of the ARIMA (2,0, 3) (2, 0, 2) time series model were 0.78 and 0.73, respectively. Using Tansig transfer functions, the Levenberg-Marquardt algorithm for training and an MLP neural network with 10 neurons in a hidden layer were developed. R2 and IA between the measured and predicted data were 0.94 and 0.91, respectively. Consequently, the results of the MLP were more reliable than the Box-Jenkins time series to predict TDS in the river.